Artificial intelligence: a critical review of current applications in pancreatic imaging

Abstract

The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.

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Abbreviations

3D:

Three dimensional

AI:

Artificial intelligence

AIP:

Autoimmune pancreatitis

ANN:

Artificial neural network

AUC:

Area under receiver operating characteristic curve

CAD:

Computer-aided diagnosis

CCN:

Convolutional neural networks

CI:

Confidence interval

CT:

Computed tomography

DDLS:

Discriminative dictionary learning for segmentation

DL:

Deep learning

DSC:

Dice similarity coefficient

IPMN:

Intraductal papillary mucinous neoplasm

MCN:

Mucinous cystic neoplasms

ML:

Machine learning

MRI:

Magnetic resonance imaging

PDAC:

Pancreatic ductal adenocarcinoma

RF:

Random forests

SCN:

Serous cystic neoplasms

SPEN:

Solid pseudopapillary epithelial neoplasms

SVM:

Support vector machines

VOI:

Volume of interest

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Correspondence to Philippe Soyer.

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The original online version of this article was revised due to interchange of Figure 1 and Figure 2 captions.

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Barat, M., Chassagnon, G., Dohan, A. et al. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol (2021). https://doi.org/10.1007/s11604-021-01098-5

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Keywords

  • Artificial intelligence
  • Pancreatic neoplasms
  • Radiomics
  • Texture analysis